<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quantitative on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/tags/quantitative/</link><description>Recent content in Quantitative on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Mon, 21 Sep 2026 01:30:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/tags/quantitative/index.xml" rel="self" type="application/rss+xml"/><item><title>Quantitative Learning Path Selection: WQU vs. QuantConnect</title><link>https://blog.lynxflow.co/en/posts/quant-learning-path-wqu-quantconnect/</link><pubDate>Mon, 21 Sep 2026 01:30:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/quant-learning-path-wqu-quantconnect/</guid><description>&lt;img src="https://blog.lynxflow.co/images/quant-learning-path-wqu-quantconnect.webp" alt="Featured image of post Quantitative Learning Path Selection: WQU vs. QuantConnect" /&gt; Conclusion first: WQU and QuantConnect solve two different problems. WQU fills in your knowledge system; QuantConnect trains your research workflow. Using either one alone leaves gaps.
Most self-taught quant learners have walked the same crooked path: run a backtest, get a Sharpe of 3.7, get excited, go live, blow up the account. The problem usually isn&amp;rsquo;t that the strategy was written down — it&amp;rsquo;s that the person who wrote it can&amp;rsquo;t articulate why they did it that way.</description></item></channel></rss>